机器人空间感知基础:分层表示和实时系统

Nathan Hughes, Yun Chang, Siyi Hu, Rajat Talak, Rumaia Abdulhai, Jared Strader, Luca Carlone
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摘要

三维空间感知是利用传感器数据和先验知识实时构建和维护可操作的持久环境表示的问题。尽管机器人感知技术发展迅速,但大多数现有方法要么是构建纯几何地图(如传统的 SLAM),要么是构建 "平面 "度量-语义地图,无法扩展到大型环境或大型语义标签字典。本文的第一部分与表征有关:我们表明,空间感知的可扩展表征需要具有层次性。分层表示法存储效率高,可生成具有较小树宽的分层图,从而实现可证明的高效推理。随后,我们介绍了室内环境分层表示法的一个例子,即三维场景图,并讨论了它的结构和特性。论文的第二部分侧重于机器人探索环境时增量构建三维场景图的算法。我们的算法结合了三维几何(例如,将自由空间聚类为场所图)、拓扑(将场所聚类为房间)和几何深度学习(例如,对机器人移动过的房间类型进行分类)。论文的第三部分侧重于在长期运行过程中维护和修正三维场景图的算法。我们提出了用于循环闭合检测的分层描述符,并介绍了如何通过解决三维场景图优化问题来修正场景图,以应对循环闭合。最后,我们将提出的感知算法结合到实时空间感知系统 Hydra 中,该系统可实时从视觉惯性数据中构建三维场景图。我们在由 Clearpath Jackal 机器人和 Unitree A1 机器人收集的照片逼真模拟和真实数据中展示了 Hydra 的性能。我们在 https://github.com/MIT-SPARK/Hydra 发布了 Hydra 的开源实现。
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Foundations of spatial perception for robotics: Hierarchical representations and real-time systems
3D spatial perception is the problem of building and maintaining an actionable and persistent representation of the environment in real-time using sensor data and prior knowledge. Despite the fast-paced progress in robot perception, most existing methods either build purely geometric maps (as in traditional SLAM) or “flat” metric-semantic maps that do not scale to large environments or large dictionaries of semantic labels. The first part of this paper is concerned with representations: we show that scalable representations for spatial perception need to be hierarchical in nature. Hierarchical representations are efficient to store, and lead to layered graphs with small treewidth, which enable provably efficient inference. We then introduce an example of hierarchical representation for indoor environments, namely a 3D scene graph, and discuss its structure and properties. The second part of the paper focuses on algorithms to incrementally construct a 3D scene graph as the robot explores the environment. Our algorithms combine 3D geometry (e.g., to cluster the free space into a graph of places), topology (to cluster the places into rooms), and geometric deep learning (e.g., to classify the type of rooms the robot is moving across). The third part of the paper focuses on algorithms to maintain and correct 3D scene graphs during long-term operation. We propose hierarchical descriptors for loop closure detection and describe how to correct a scene graph in response to loop closures, by solving a 3D scene graph optimization problem. We conclude the paper by combining the proposed perception algorithms into Hydra, a real-time spatial perception system that builds a 3D scene graph from visual-inertial data in real-time. We showcase Hydra’s performance in photo-realistic simulations and real data collected by a Clearpath Jackal robots and a Unitree A1 robot. We release an open-source implementation of Hydra at https://github.com/MIT-SPARK/Hydra .
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